Schenk_Brandenberger_S10_Aufg1.py

@@ -1,25 +0,0 @@
-import numpy as np
-
-
-print("*********Jacobi-Verfahren*********")
-def Jacobi(A,b,x,steps):
-    L = np.tril(A,k=-1)
-    D = np.diag(np.diag(A))
-    R = np.triu(A, k=1)
-    for steps in range(steps):
-        x = -np.linalg.inv(D) @ (L + R) @ x + np.linalg.inv(D) @ b
-    #print(np.linalg.norm((-np.linalg.inv(D) @ (L + R)),np.inf))
-    return x
-
-
-
-A = np.array([[8,5,2],[5,9,1],[4,2,7]])
-b = np.array([[19],[5],[34]])
-x = np.array([[1],[-1],[3]])
-steps = 3
-print(Jacobi(A,b,x,steps))
-print("Sum of vector:")
-#print(np.sum(Jacobi(A,b,x,steps)))
-#print(np.linalg.norm(Jacobi(A,b,x,3)-Jacobi(A,b,x,2),np.inf)*7)
-print(np.linalg.norm(Jacobi(A,b,x,3)-Jacobi(A,b,x,2),np.inf))
-print((0.0001*0.125)/0.7693452380952384)

Schenk_Brandenberger_S10_Aufg2.py

@@ -1,21 +0,0 @@
-import numpy as np
-
-print("*********Gauss Seidel*********")
-def Gauss_Seidel(A,b,x,steps):
-    L = np.tril(A,k=-1)
-    D = np.diag(np.diag(A))
-    R = np.triu(A, k=1)
-    print(np.linalg.norm(-np.linalg.inv(D+L) @ R,np.inf))
-    for steps in range(steps):
-        x = -np.linalg.inv(D+L) @ R @ x + np.linalg.inv(D+L) @ b
-    return x
-
-
-A = np.array([[8,5,2],[5,9,1],[4,2,7]])
-b = np.array([[19],[5],[34]])
-x = np.array([[1],[-1],[3]])
-steps = 3
-print(Gauss_Seidel(A,b,x,steps))
-print("Sum of vector:")
-print(np.sum(Gauss_Seidel(A,b,x,steps)))
-print(np.linalg.norm(Gauss_Seidel(A,b,x,3)-Gauss_Seidel(A,b,x,2),np.inf))